• 기술혁신학회지
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• 제5권2호
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• pp.167-188
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• 2002

Summary: This paper analyzes Indian software industry in the perspective of innovation cluster. The research shows that the software industry has been following an upstream clustering process, where the major value activity is expanding from low value product/services to high value product/services. The growth of software industry could be successful because there was appropriate initial condition of Bangalore, such as the availability of high qualified human resources, excellent research institutes, small high-tech companies. The role of government was helpful for the late growth of software industry but not a critical factor for the initial development of the S/W cluster. It is suggested that government should consider the initial condition of a concerned location critically to implement a cluster-type innovation policy.

• 한국지능시스템학회논문지
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• 제14권5호
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• pp.517-524
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• 2004

1960년대 퍼지 이론이 소개된 이후 데이터 마이닝을 포함한 기계 학습 분야의 군집화 작업에서 퍼지 이론이 폭넓게 사용되었다. 퍼지 C-평균 알고리즘은 가장 많이 사용되는 퍼지 군집화 알고리즘이다. 이 알고리즘은 하나의 데이터 개체가 서로 다른 소속 정도를 가지고 각 군집에 할당될 수 있도록 한다. 퍼지 C-평균 알고리즘도 K-평균 알고리즘과 같은 일반적인 군집화 알고리즘과 마찬가지로 초기 군집수와 군집 중심의 위치에 의해 최종 군집 결과의 성능 차이가 나타난다. 군집화를 위한 이러한 초기 설정은 주관적이며 이 때문에 적절치 못한 결과를 얻게 될 수도 있다. 본 논문에서는 이 문제를 해결할 수 있는 방법으로 주어진 학습 데이터의 속성을 기반으로 한 초기 군집수와 군집 중심을 결정하는 개선된 밀도 기반의 퍼지 C-평균 알고리즘을 제안하였다. 제안 방법은 격자를 사용하여 초기 군집 중심의 위치와 군집수를 결정하였다. 기존에 많이 이용되었던 객관적인 기계 학습 데이터를 이용하여 제안 알고리즘의 성능비교를 수행하였다.

• 정보관리학회지
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• 제21권4호
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• pp.173-185
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• 2004

정보통신의 기술이 발달하면서 정보의 양이 많아지고 사용자의 질의에 대한 검색 결과 리스트도 많이 추출되므로 빠르고 고품질의 문서 클러스터링 알고리즘이 중요한 역할을 하고 있다. 많은 논문들이 계층적 클러스터링 방법을 이용하여 좋은 성능을 보이지만 시간이 많이 소요된다. 반면 K-means 알고리즘은 시간 복잡도를 줄일 수 있는 방법이다. 본 논문에서는 계층적 클러스터링 시스템인 콘도르(Condor) 시스템에서 간단하고 고품질이며 효율적으로 정보 검색 할 수 있도록 구현하였다. 이 시스템은 K-Means Algorithm을 이용하였으며 클러스터 계층 깊이와 초기값을 조절하여 $88\%$의 정확율을 보였다.

• 한국지능시스템학회논문지
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• 제13권1호
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• pp.12-17
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• 2003

데이터의 군집화를 수행할 때 최적 군집수 결정은 군집 결과의 성능에 많은 영향을 미친다. 특히 K-means 방법에서는 초기 군집수 K에 따라 군집결과의 성능 차이가 많이 나타난다. 하지만 대다수의 군집분석에서 초기 군집수의 결정은 경험을 바탕으로 하여 주관적으로 결정된다. 이때 개체수와 속성수가 증가하면 이러한 결정은 더욱 어려워지며 이때 결정된 군집수가 최적이 된다는 보장도 없다. 본 논문에서는 군집의 수를 자동으로 결정하고 그 결과의 유효성을 보장하기 위해 유전자 알고리즘에 기반한 최적 군집수 결정 방안을 제안한다. 데이터의 속성에 근거한 초기 해 집단이 생성되고, 해 집단 내에서 최적화된 군집수를 찾기 위해 교차 연산이 이루어진다. 적합도 값은 전체 군집화의 비 유사성의 합의 역으로 결정되어 전체적인 군집화 성능이 향상되는 방향으로 수렴된다. 또한 지역 국소값을 해결하기 위해 돌연변이 연산이 사용된다. 그리고 유전자 알고리즘의 학습 시간의 비용을 줄이기 위해 붓스트랩 기법이 적용된다

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -1
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• pp.450-453
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• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 steps. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional (3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with it’s initial centroid value as the outstanding cluster’s centroid value. The proposed segmentation algorithm complements the defect of FCM algorithm, being influenced upon initial centroid, by calculating cluster’s centroid accurately And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the results of single spectral analysis.

• 천문학회지
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• 제44권2호
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• pp.39-48
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• 2011

UBV I CCD photometry of NGC 2353 is performed as a part of the "Sejong Open cluster Survey" (SOS). Using photometric membership criteria we select probable members of the cluster. We derive the reddening and distance to the cluster, i.e., E(B - V ) = 0.10 ${\pm}$ 0.02 mag and 1.17 ${\pm}$ 0.04 kpc, respectively. We find that the projected distribution of the probable members on the sky is elliptical in shape rather than circular. The age of the cluster is estimated to be log(age)=8.1 ${\pm}$ 0.1 in years, older than what was found in previous studies. The minimum value of binary fraction is estimated to be about 48 ${\pm}$ 5 percent from a Gaussian function fit to the distribution of the distance moduli of the photometric members. Finally, we also obtain the luminosity function and the initial mass function (IMF) of the probable cluster members. The slope of the IMF is ${\Gamma}=-1.3{\pm}0.2$.

• 한국프랜차이즈경영연구
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• 제7권1호
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• pp.21-29
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• 2016

Purpose - The primary purpose of this study is to employ effective marketing methods using market segmentation of coffee shops by determining how motivations to visit coffee shops have different impacts on demographic profile of visitors and characteristics of coffee shop visits, so as to draw out a better understanding of customers of coffee market. Research design, data, and methodology - Data were collected using surveys of self-administered questionnaires toward coffee shop users in Daejeon, Korea. A number of samples used in data analysis were 253 excluding unusable responses. The data were analyzed through frequency, reliability, and factor analysis using SPSS 20.0. Factor analysis was conducted through the principal component analysis and varimax rotation method to derive factors of one or more eigen values. In addition, the cluster analysis, multivariate ANOVA, and cross-tab analysis were used for the market segmentation based on the types of motivation for coffee shop visits. The process of the cluster analysis is as follows. Four clusters were derived through hierarchical clustering, and k-means cluster analysis was then carried out using mean value of the four clusters as the initial seed value. Result - The factor analysis delineated four dimensions of motivation to visit coffee shops: ostentation motivation, hedonic motivation, esthetic motivation, utility motivation. The cluster analysis yielded four clusters: utility and esthetic seekers, hedonic seekers, utility seekers, ostentation seekers. In order to further specify the profile of four clusters, each cluster was cross tabulated with socio-demographics and characteristics of coffee shop visits. Four clusters are significantly different from each other by four types of motivations for coffee shop visits. Conclusions - This study has empirically examined the difference in demographic profile of visitors and characteristics of coffee shop visits by motivation to visit coffee shops. There are significant differences according to age, education background, marital status, occupation and monthly income. In addition, coffee shops use pattern characterization in frequency of visits to coffee shops, relationships with companion, purpose of visit, information sources, brand type, average expense per visit, important elements of selection attribute were significantly different depending on motivations for coffee shop visits.

• 한국통신학회논문지
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• 제33권3B호
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• pp.123-134
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• 2008

본 논문에서는 센서 노드의 에너지 소모의 효율성을 증대시키는 무선 센서 네트워크에 대한 라우팅 알고리즘을 제안한다. 각 센서 노드는 멀티 홉 센서 필드에서 최초의 플러딩 과정을 통해 싱크 노드로의 최소 홉수를 나타내는 플러딩 레벨 값을 얻는다. 이 값은 싱크 노드로의 연결을 보장하고 클러스터를 구성하는 동안 사용되며 라우팅 과정에서 효과적으로 사용되어 에너지 효율성을 증가시킨다. 이 알고리즘은 분석과 많은 실험을 통해 성능평가가 이루어진다.

• 천문학회지
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• 제40권4호
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• pp.157-160
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• 2007

I present a model to explain the mass segregation and shallow mass functions observed in the central parts of starburst stellar clusters. The model assumes that the initial pre-stellar cores mass function resulting from the turbulent fragmentation of the proto-cluster cloud is significantly altered by the cores coalescence before they collapse to form stars. With appropriate, yet realistic parameters, this model based on the competition between cores coalescence and collapse reproduces the mass spectra of the well studied Arches cluster. Namely, the slopes at the intermediate and high mass ends, as well as the peculiar bump observed at $6M_{\bigodot}$. This coalescence-collapse process occurs on a short timescale of the order of the free fall time of the proto-cluster cloud (i.e., a few $10^4$ years), suggesting that mass segregation in Arches and similar clusters is primordial. The best fitting model implies the total mass of the Arches cluster is $1.45{\times}10^5M_{\bigodot}$, which is slightly higher than the often quoted, but completeness affected, observational value of a few $10^4M_{\bigodot}$. The model implies a star formation efficiency of ${\sim}30$ percent which implies that the Arches cluster is likely to a gravitationally bound system.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.185-194
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• 2020

Designing of a hierarchical clustering algorithm is one of the numerous approaches to minimize the energy consumption of the Wireless Sensor Networks (WSNs). In this paper, a homogeneous and randomly deployed sensor nodes is considered. These sensors are energy constrained elements. The nominal selection of the Cluster Head (CH) which falls under the clustering part of the network protocol is studied and compared to Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. CHs in this proposed process is the function of total remaining energy of each node as well as total average energy of the whole arrangement. The algorithm considers initial energy, optimum value of cluster heads to elect the next group of cluster heads for the network as well as residual energy. Total remaining energy of each node is compared to total average energy of the system and if the result is positive, these nodes are eligible to become CH in the very next round. Analysis and numerical simulations quantify the efficiency and Average Energy Ratio (AER) of the proposed system.